Crop yield estimation and mapping are important tools that can help growers efficiently use their available resources and have access to detailed representations of their farm. Technical advancements in computer vision have improved the detection, quality assessment and yield estimation processes for crops, including apples, citrus, mangoes, maize, figs and many other fruits. However, similar methods capable of exporting a detailed yield map for vegetable crops have not yet been fully developed. A machine vision-based yield monitor was designed to perform identification and continuous counting of shallot onions in-situ during the harvesting process. The system is composed of a video and position logger, coupled with acomputer software, and can be used within the tractor itself.  A modular camera bracket collected video data of the crops while positioned directly above the harvesting conveyor. Video data was collected in real-time with natural sunlight conditions and in a semi-controlled lighting environment using an artificial light source to enhance vegetable areas. Computational analysis was performed to track detected vegetables on the conveyor. The system is to be tested for a full continuous run during the summer 2018 harvesting season. Based on preliminary results, occasional occlusion of vegetables and inconsistent light conditions are the main limiting factors that may inhibit performance. Although further enhancements are envisioned for the prototype system developed, it has the potential to benefit many producers of small vegetable crops by providing them with useful harvest information in real time and can help to improve harvesting logistics.